SYSTEM AND METHOD FOR USE WITH A DATA ANALYTICS ENVIRONMENT TO DETERMINE A PROBABILITY OF FAILURE OR DOWNTIME IN WORK ORDERS

Description

CLAIM OF PRIORITY AND CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. Provisional Patent Application titled “SYSTEM AND METHOD FOR USE WITH A DATA ANALYTICS ENVIRONMENT TO DETERMINE A PROBABILITY OF FAILURE OR DOWNTIME IN WORK ORDERS”, Application No. 63/690,593, filed Sep. 4, 2024; which above application and the contents thereof are herein incorporated by reference.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.

TECHNICAL FIELD

Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to determine a probability of failure or downtime in work orders.

BACKGROUND

Generally described, data analytics enables the computer-based examination of an amount of data, to derive an analytic data, metrics, conclusions, or other types of analytical information from, or descriptive of, the source data. Systems and methods can be used, for example, to generate an analytic business intelligence data, such as a set of data metrics or measures operating as key performance indicators, which analytically describe an organization's business-related data in a format useful to its decision-makers.

SUMMARY

Embodiments described herein are generally related to data analytics environments, and are particularly directed to systems and methods for use with a data analytics environment to determine a probability of failure or downtime in work orders. In accordance with an embodiment, an example method can provide access to a work order application at a data analytics environment, the work order application providing a work order canvas at which a work order comprising an instance of a work order asset is identified. The method can generate, by a prediction engine of the data analytics environment, an indication of a likelihood of success of the work order, wherein the prediction engine utilizes data associated with the instance of the work order asset to provide the indication of the likelihood of success. The method can provide the indication of the likelihood of success of the work order via an interface.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for providing a cloud infrastructure or data analytics environment, in accordance with an embodiment.

FIG. 2 further illustrates a system for providing a cloud infrastructure or data analytics environment, in accordance with an embodiment.

FIG. 3 illustrates an example use of the system to provide a data analytics environment, in accordance with an embodiment.

FIG. 4 further illustrates an example data analytics environment, in accordance with an embodiment.

FIG. 5 further illustrates an example data analytics environment, in accordance with an embodiment.

FIG. 6 further illustrates an example data analytics environment, in accordance with an embodiment.

FIG. 7 further illustrates an example data analytics environment, in accordance with an embodiment.

FIG. 8 further illustrates an example data analytics environment, in accordance with an embodiment.

FIG. 9 further illustrates an example data analytics environment, including the use of a large language model, in accordance with an embodiment.

FIG. 10 further illustrates an example data analytics environment, including the use of retrieval-augmented generation, in accordance with an embodiment.

FIG. 11 illustrates a system for use with a data analytics environment to determine probability of down failure or downtime in work orders, in accordance with an embodiment.

FIG. 12 illustrates a system for use with a data analytics environment to determine probability of down failure or downtime in work orders, in accordance with an embodiment.

FIG. 13 illustrates a system for use with a data analytics environment to determine probability of down failure or downtime in work orders, in accordance with an embodiment.

FIG. 14 illustrates a system for use with a data analytics environment to determine probability of down failure or downtime in work orders, in accordance with an embodiment.

FIG. 15 illustrates a screenshot produced by a system for use with a data analytics environment to determine a probability of failure or downtime in work orders, in accordance with an embodiment.

FIG. 16 illustrates a screenshot produced by a system for use with a data analytics environment to determine a probability of failure or downtime in work orders, in accordance with an embodiment.

FIG. 17 is a flowchart of a method for use with a data analytics environment to determine a probability of failure or downtime in work orders, in accordance with an embodiment.

DETAILED DESCRIPTION

Generally described, within an organization, data analytics enables computer-based examination of large amounts of data, for example to derive conclusions or other information from the data. For example, business intelligence (BI) tools can be used to provide users with business intelligence describing their enterprise data, in a format that enables the users to make strategic business decisions.

Increasingly, data analytics can be provided within the context of enterprise software application environments, such as, for example, an Oracle Fusion Applications environment; or within the context of software-as-a-service (SaaS) or cloud environments, such as, for example, an Oracle Analytics Cloud or Oracle Cloud Infrastructure environment; or other types of analytics application or cloud environments.

Examples of data analytics environments and business intelligence tools/servers include Oracle Business Intelligence Server (OBIS), Oracle Analytics Cloud (OAC), and Fusion Analytics Warehouse (FAW), which support features such as data mining or analytics, and analytic applications.

Cloud Infrastructure Environments

FIGS. 1 and 2 illustrate a system for providing a cloud infrastructure or data analytics environment, in accordance with an embodiment.

In accordance with an embodiment, the components and processes illustrated in FIG. 1, and as further described herein with regard to various embodiments, can be provided as software or program code executable by a computer system or other type of processing device, for example a cloud computing system, or other suitably-programmed computer system.

The illustrated example is provided for purposes of illustrating a computing environment which can be used to provide dedicated or private label cloud environments, for use by tenants of a cloud infrastructure in accessing subscription-based software products, services, or other offerings associated with the cloud infrastructure environment. In accordance with other embodiments, the various components, processes, and features described herein can be used with other types of cloud computing environments.

As illustrated in FIG. 1, in accordance with an embodiment, a cloud infrastructure or data analytics environment 100 can operate on a cloud computing infrastructure 101 comprising hardware (e.g., processor, memory), software resources, and one or more cloud interfaces 4 or other application program interfaces (API) that provide access to the shared cloud resources via one or more load balancers 6.

In accordance with an embodiment, the cloud infrastructure environment supports the use of availability domains, such as, for example, availability domains A 80, B 82, which enables customers to create and access cloud networks 84, 86, and run cloud instances A 92, B 94.

In accordance with an embodiment, a tenancy can be created for each cloud tenant/customer, for example tenant A 42, B 44, which provides a secure and isolated partition within the cloud infrastructure environment within which the customer can create, organize, and administer their cloud resources. A cloud tenant/customer can access an availability domain and a cloud network to access each of their cloud instances.

In accordance with an embodiment, a client device, such as, for example, a computing device 10 having a device hardware 11 (e.g., processor, memory), application 14 and graphical user interface 12, can enable an administrator other user to communicate with the cloud infrastructure environment via a network such as, for example, a wide area network, local area network, or the Internet, to create or update cloud services.

In accordance with an embodiment, the cloud infrastructure environment provides access to shared cloud resources 40 via, for example, a compute resources layer 50, a network resources layer 64, and/or a storage resources layer 70. Customers can launch cloud instances as needed, to meet compute and application requirements. After a customer provisions and launches a cloud instance, the provisioned cloud instance can be accessed from, for example, a client device.

In accordance with an embodiment, the compute resources layer can comprise resources, such as, for example, bare metal cloud instances 52, virtual machines 54, graphical processing unit (GPU) compute cloud instances 57, and/or containers 58. The compute resources layer can be used to, for example, provision and manage bare metal compute cloud instances, or provision cloud instances as needed to deploy and run applications, as in an on-premises data center.

For example, in accordance with an embodiment, the cloud infrastructure environment can provide control of physical host (bare metal) machines within the compute resources layer, which run as compute cloud instances directly on bare metal servers, without a hypervisor.

In accordance with an embodiment, the cloud infrastructure environment can also provide control of virtual machines within the compute resources layer, which can be launched, for example, from an image, wherein the types and quantities of resources available to a virtual machine cloud instance can be determined, for example, based upon the image that the virtual machine was launched from.

In accordance with an embodiment, the network resources layer can comprise a number of network-related resources, such as, for example, virtual cloud networks (VCNs) 65, load balancers 67, edge services 68, and/or connection services 69.

In accordance with an embodiment, the storage resources layer can comprise a number of resources, such as, for example, data/block volumes 72, file storage 74, object storage 76, and/or local storage 78.

In accordance with an embodiment, the cloud environment can include a container orchestration system, and container orchestration system API, that enables containerized application workflows to be deployed to a container orchestration environment, for example a Kubernetes (k8s) cluster.

For example, in accordance with an embodiment, the cloud environment can be used to provide containerized compute cloud instances within the compute resources layer, and a container orchestration implementation (e.g., Oracle Cloud Infrastructure Container Engine for Kubernetes (OKE)), can be used to build and launch containerized applications or cloud-native applications, specify compute resources that the containerized application requires, and provision the required compute resources.

As illustrated in FIG. 2, in accordance with an embodiment, the cloud infrastructure or data analytics environment can include a range of complementary cloud-based components, for example as cloud infrastructure applications and services 111, that enable organizations or enterprise customers to operate their applications and services in a highly-available hosted environment.

By way of example, in accordance with an embodiment, a self-contained cloud region can be provided as a complete, e.g., Oracle Cloud Infrastructure (OCI) dedicated region within an organization's data center that offers the data center operator the agility, scalability, and economics of a public cloud, while retaining full control of their data and applications to meet security, regulatory, or data residency requirements.

Data Analytics Environments

FIG. 3 illustrates an example use of the system to provide a data analytics environment, in accordance with an embodiment.

The example embodiment illustrated in FIG. 3 is provided for purposes of illustrating an example of a data analytics environment in association with which various embodiments described herein can be used. In accordance with other embodiments and examples, the approach described herein can be used with other types of data analytics, database, or data warehouse environments.

As illustrated in FIG. 3, in accordance with an embodiment, a data analytics environment 100 can be provided by, or otherwise operate at, a computer system having a computer hardware (e.g., processor, memory) 101, and including one or more software components operating as a control plane 102, and a data plane 104, and providing access in the manner of a data layer to a data warehouse instance 160 (e.g., having a database 161, or other type of data source).

In accordance with an embodiment, the control plane operates to provide control for cloud or other software products offered within the context of a cloud environment. For example, in accordance with an embodiment, the control plane can include a console interface 110 that enables access by a customer (tenant) and/or a cloud environment having a provisioning component 111, for example to allow customers to provision services for use within their enterprise environment. The provisioning component can provision a data warehouse instance, including a customer schema of the data warehouse; and populate the data warehouse instance with the appropriate information supplied by the customer.

In accordance with an embodiment, the data plane can include a data pipeline or process layer 120 and a data transformation layer 134, that together process data from an organization's enterprise software environment, and load a transformed data into the data warehouse. The data transformation layer can include a data model, such as, for example, a knowledge model (KM), or other type of data model, that the system uses to transform the data received from business applications and corresponding databases, into a model format understood by the data analytics environment. The data plane is responsible for performing extract, transform, and load (ETL) operations, including extracting data from an organization's enterprise software environment, transforming the extracted data into a model format, and loading the transformed data into a customer schema of the data warehouse.

For example, in accordance with an embodiment, each customer (tenant) of the environment can be associated with their own customer schema; and can be additionally provided with read-only access to the data analytics schema, which can be updated by a data pipeline or process, for example, an ETL process, on a periodic or other basis. For example, a data pipeline or process can be scheduled to execute at intervals (e.g., hourly/daily/weekly) to extract enterprise data 103 from an enterprise software environment, such as, for example, business productivity software applications and corresponding databases 106.

In accordance with an embodiment, an extract process 108 can extract the data, whereupon extraction the data pipeline or process can insert extracted data into a data staging area, which can act as a temporary staging area for the extracted data. When the extract process has completed its extraction, the data transformation layer can be used to transform the extracted data into a model format to be loaded into the customer schema of the data warehouse. During the data transformation, the system can perform dimension generation, fact generation, and aggregate generation, as appropriate. Dimension generation can include generating dimensions or fields for loading into the data warehouse instance.

In accordance with an embodiment, after transformation of the extracted data, the data pipeline or process can execute a warehouse load procedure 150, to load the transformed data into the customer schema of the data warehouse instance. Subsequent to the loading of the transformed data into customer schema, the transformed data can be analyzed and used in a variety of additional business intelligence processes.

Different customers may have different requirements with regard to how their data is classified, aggregated, or transformed, for providing data analytics or business intelligence data, or developing software analytic applications. In accordance with an embodiment, to support such different requirements, a semantic layer 180 can include data defining a semantic model of a customer's data; which is useful in assisting users in understanding and accessing that data using commonly-understood business terms; and provide custom content to a presentation layer 190.

In accordance with an embodiment, a customer may perform modifications to their data source model, to support their particular requirements, for example by adding custom facts or dimensions associated with the data stored in their data warehouse instance; and the system can extend the semantic model accordingly. A semantic model can be defined, for example, in an Oracle environment, as a BI Repository (RPD) file, having metadata that defines logical schemas, physical schemas, physical-to-logical mappings, aggregate table navigation, and/or other constructs that implement the various physical layer, business model and mapping layer, and presentation layer aspects of the semantic model.

In accordance with an embodiment, the presentation layer can enable access to the data content using, for example, a software analytic application, user interface, analytics dashboard, key performance indicators (KPI's); or other type of report or interface as may be provided by products such as, for example, Oracle Analytics Cloud, or Oracle Analytics for Applications.

In accordance with an embodiment, a query engine 18 (e.g., an Oracle Business Intelligence Server, OBIS instance) operates in the manner of a federated query engine to serve analytical queries or requests from clients directed to data stored at a database. The query engine can push down operations to supported databases, in accordance with a query execution plan 56, wherein a logical query can include Structured Query Language (SQL) statements received from the clients; while a physical query includes database-specific statements that the query engine sends to the database to retrieve data when processing the logical query.

In accordance with an embodiment, a user/developer can interact with a client computer device 10 that includes a computer hardware 11 (e.g., processor, storage, memory), user interface 12, and client application 14. A query engine or business intelligence server generally operates to process inbound, e.g., SQL, requests against a database model, build and execute one or more physical database queries, process the data appropriately, and return the data in response to the request.

To accomplish this, in accordance with an embodiment, the query engine can include a logical or business model, or metadata, that describes the data available as subject areas for queries; a request generator that takes incoming queries and turns them into physical queries for use with a connected data source; and a navigator that takes the incoming query, navigates the logical model and generates those physical queries that best return the data required for a particular query.

For example, in accordance with an embodiment, the query engine may employ a logical model mapped to data in a data warehouse, by creating a simplified star schema business model over various data sources so that the user can query data as if it originated at a single source. The information can then be returned to the presentation layer as subject areas, according to business model layer mapping rules.

In accordance with an embodiment, the query engine can process queries against a database according to a query execution plan. During operation the query engine can create a query execution plan which can then be further optimized, for example to perform aggregations of data necessary to respond to a request. Data can be combined together and further calculations applied, before the results are returned to the calling application.

In accordance with an embodiment, a request for data analytics or visualization information can be received via a client application and user interface as described above, and communicated to the data analytics environment (in the example of a cloud environment, via a cloud service). The system can retrieve an appropriate dataset to address the user/business context, for use in generating and returning the requested data analytics or visualization information to the client, as a data visualization 196.

In accordance with an embodiment, a client application can be implemented as software or computer-readable program code executable by a computer system or processing device, and having a user interface, such as, for example, a software application user interface or a web browser interface. The client application can retrieve or access data via an Internet/HTTP or other type of network connection to the data analytics environment, or in the example of a cloud environment via a cloud service provided by the environment.

FIG. 4 further illustrates an example data analytics environment, in accordance with an embodiment.

As illustrated in FIG. 4, in accordance with an embodiment, the data analytics environment enables a dataset to be retrieved, received, or prepared from one or more data source(s) 198, for example via one or more data source connections. Examples of the types of data that can be transformed, analyzed, or visualized using the systems and methods described herein include data directed to Enterprise Resource Planning (ERP), Human Capital Management (HCM), or Human Resources (HR), or other types of data provided at one or more of a database, data storage service, or other type of data repository or data source.

For example, in accordance with an embodiment, a request for data analytics or visualization information can be received via a client application and user interface as described above, and communicated to the data analytics environment, for example via a cloud service. The system can retrieve an appropriate dataset to address the user/business context, for use in generating and returning the requested data analytics or visualization information to the client.

FIG. 5 further illustrates an example data analytics environment, in accordance with an embodiment.

As illustrated in FIG. 5, in accordance with an embodiment, data can be sourced, e.g., from a customer's (tenant's) enterprise software environment (106), using the data pipeline process; or as custom data 109 sourced from one or more customer-specific applications 107; and loaded to a data warehouse instance, including in some examples the use of an object storage 105 for storage of the data. A user can create a dataset that uses tables from different connections and schemas. The system uses the relationships defined between these tables to create relationships or joins in the dataset.

In accordance with an embodiment, the data warehouse can include a default data analytics schema 162 and, for each customer (tenant) of the system, a customer schema 164. For each customer (tenant), the system uses the data analytics schema that is maintained and updated by the system, within a system/cloud tenancy 114, to pre-populate a data warehouse instance for the customer, based on an analysis of the data within that customer's enterprise applications environment, and within a customer tenancy 117. As such, the data analytics schema maintained by the system enables data to be retrieved, by the data pipeline or process, from the customer's environment, and loaded to the customer's data warehouse instance.

In accordance with an embodiment, the system also provides, for each customer of the environment, a customer schema that allows the customer to supplement and utilize the data within their own data warehouse instance. For each customer, their resultant data warehouse instance operates as a database whose contents are partly-controlled by the customer; and partly-controlled by the environment (system).

For example, in accordance with an embodiment, a data warehouse can include a data analytics schema and, for each customer/tenant, a customer schema sourced from their enterprise software environment. The data provisioned in a data warehouse tenancy is accessible only to that tenant; while at the same time allowing access to various, e.g., ETL-related or other features of the shared environment.

In accordance with an embodiment, for a particular customer/tenant, upon extraction of their data, the data pipeline or process can insert the extracted data into a data staging area for the tenant, which can act as a temporary staging area for the extracted data. When the extract process has completed its extraction, the data transformation layer can be used to transform the extracted data into a model format to be loaded into the customer schema of the data warehouse.

FIG. 6 further illustrates an example data analytics environment, in accordance with an embodiment.

As illustrated in FIG. 6, in accordance with an embodiment, the process of extracting data from a customer's (tenant's) enterprise software environment, and loading the data to a data warehouse instance, or refreshing the data in a data warehouse, generally involves several stages, performed by an ETP service 160 or process, including one or more extraction service 163; transformation service 165; and load/publish service 167, executed by one or more compute instance(s) 170.

For example, in accordance with an embodiment, extracted files can be uploaded to an object storage component for storage of the data. The transformation process then applies a business logic while loading them to a target data warehouse, e.g., an Autonomous Data Warehouse (ADW) database, which is internal to the data pipeline or process, and is not exposed to the customer (tenant). A load/publish service or process takes the data from the ADW database and publishes it to a data warehouse instance that is accessible to the customer (tenant).

FIG. 7 further illustrates an example data analytics environment, in accordance with an embodiment.

As illustrated in FIG. 7, in accordance with an embodiment, the data pipeline or process maintains, for each of a plurality of customers (tenants), for example customer A 180, customer B 182, a data analytics schema that is updated on a periodic basis, by the system in accordance with best practices for a particular analytics use case. For each of a plurality of customers (e.g., customers A, B), the system uses the data analytics schema 162A, 162B, that is maintained and updated by the system, to pre-populate a data warehouse instance for the customer, based on an analysis of the data within that customer's enterprise applications environment 106A, 106B, and within each customer's tenancy (e.g., customer A tenancy 181, customer B tenancy 183); so that data is retrieved, by the data pipeline or process, from the customer's environment, and loaded to the customer's data warehouse instance 160A, 160B.

In accordance with an embodiment, the data analytics environment also provides, for each of a plurality of customers of the environment, a customer schema (e.g., customer A schema 164A, customer B schema 164B) that allows the customer to supplement and utilize the data within their own data warehouse instance.

As described above, in accordance with an embodiment, for each of a plurality of customers of the data analytics environment, their resultant data warehouse instance operates as a database whose contents are partly-controlled by the customer; and partly-controlled by the data analytics environment (system); including that their database appears pre-populated with appropriate data that has been retrieved from their enterprise applications environment to address various analytics use cases. When the extract process 108A, 108B for a particular customer has completed its extraction, the data transformation layer can be used to transform the extracted data into a model format to be loaded into the customer schema of the data warehouse.

In accordance with an embodiment, activation plans 186 can be used to control the operation of the data pipeline or process services for a customer, for a particular functional area, to address that customer's (tenant's) particular needs. For example, an activation plan can define a number of extract, transform, and load (publish) services or steps to be run in a certain order, at a certain time of day, and within a certain window of time.

FIG. 8 further illustrates an example data analytics environment, in accordance with an embodiment.

Generally described, within a database or data warehouse, the data of interest may be spread across multiple tables. In such environments, joins can be used to stitch the data from various tables together, to better prepare the data for analysis.

For example, as illustrated in FIG. 8, in accordance with an embodiment, the data analytics environment enables a dataset to be retrieved, received, or prepared from one or more data source(s), for example via one or more data source connections, fact and/or dimension tables 210, 212, 214, 216, or joins 221, 222, 224, 226, 227 between selections of dimension tables 302, 304.

In accordance with an embodiment, a request received at a data visualization environment to display analytic artifacts 192, for example as may be related to key performance indicators, analytics dashboards, or scorecards, can be received via a client application and user interface as described above, and communicated to the data analytics environment via a cloud service. The system can retrieve 232 an appropriate dataset using, e.g., SELECT statements, to address the user/business context, for use in generating and returning the requested data analytics or visualization information to the client.

Large Language Models (LLM)

FIG. 9 further illustrates an example data analytics environment, including the use of a large language model, in accordance with an embodiment.

As illustrated in FIG. 9, in accordance with an embodiment, a data analytics system can include a large language model (LLM) environment 420. A vector database 422 provides storage and retrieval of vectors or vector embeddings, which in turn enables LLMs to understand information with increased context and accuracy, for example in generating a requested data analytics information or data visualization.

In accordance with an embodiment, the system can parse a user query or natural language input, infer an intent 428 based on one or more large language model (LLM) prompt 424 or LLM processor 426, and then determine, for example, which subject areas may be relevant to the inferred intent, and generate or return an appropriate content 429.

Retrieval-Augmented Generation (RAG)

FIG. 10 further illustrates an example data analytics environment, including the use of retrieval-augmented generation, in accordance with an embodiment.

As illustrated in FIG. 10, in accordance with an embodiment, a data analytics system can include the use of retrieval-augmented generation (RAG) environment 430 that optimizes the output of a large language model (LLM) with targeted information, to provide a more contextually appropriate content in response to a user query.

In accordance with an embodiment, during the retrieval process:

Enterprise data can be received (1) in various formats, for example, as PDF, TXT, CSV, XML, or JSON documents, via REST, File, or other protocols.

The enterprise data or documents is broken into a plurality of segment or chunks (2).

Vector embeddings are obtained for each chunk of data (3), for example by calling a generative AI embedding service, or by using an embedding model.

The vector embeddings associated with the chunks of data are stored in a vector database, along with the data (4).

In accordance with an embodiment, during the augmented generation process:

The system can receive from a user, a data request or query, or a natural language input (5).

The system invokes an augmentation process or service to obtain the context for the request or query (6).

An embedding service is used to get the vector embeddings of the query data (7).

The augmentation process or service can obtain additional context based on a semantic search of the query data and its vector embedding (8).

The system can then generate an appropriate response based on the context and query (9); and return the generated response to the user (10).

The above example is provided for purpose of illustrating an example of a data analytics environment that includes the use of retrieval-augmented generation. In accordance with other embodiments, the system can include other forms of retrieval-augmented generation, which in turn can include different or other components or processes.

Probability of Failure or Downtime in Work Orders

Currently, for usual work orders submitted to, e.g., an application canvas of a cloud infrastructure environment, e.g., an Oracle Fusion Manufacturing Work Order, can involve a work flow that links one or many instances of work order equipment/assets (throughout the specification, the terms “equipment” and “asset” can be utilized interchangeably). These work order equipment/asset instances can be chosen during resource operation or resource transaction. In accordance with an embodiment, the described approach can be used to implement a proactive check mechanism by leveraging equipment's maintenance history, failure instances and equipment's meter reading data.

For example, consider: A manufacturing company produces electronic components. The production process involves several machines, including CNC machines, soldering stations, and testing equipment. A work order uses multiple equipment/asset instances to create a product. In accordance with an embodiment, for each work order, the system can implement a proactive check to select equipment instances based on their maintenance history, failure instances, and meter readings.

Currently, in some environments there is an equipment qualification check that happens during equipment instance selection while performing work order resource operation or resource transaction. In accordance with an embodiment, the systems and methods described herein can provide an additional check on the probability of asset failure can be done to allow the end user to choose the asset that has least probability for failure or downtime during the equipment's usage time for the manufacturing work order. This provides for efficiency and performance improvements.

In accordance with an embodiment, some advantages provided by the systems and methods can include: reduced downtime—by selecting the most (or more) reliable equipment, unexpected breakdowns are minimized; increased efficiency—operators use equipment(s) that are in optimal condition, enhancing productivity; and predictive maintenance—continuous monitoring allows for timely maintenance, preventing failures before they occur.

In accordance with an embodiment, the described approach provides one or more features to evaluate the equipment's probability of failure or down time before using it in the manufacturing work order resource operation or resource transaction is not currently present in manufacturing applications, such as a Fusion Manufacturing Application.

FIG. 11 illustrates a system for use with a data analytics environment to determine probability of down failure or downtime in work orders, in accordance with an embodiment.

In accordance with an embodiment, a user can interact with a computer device 10 comprise computer hardware 11, which is running an application 14 that provides a user interface 12. The user can utilize an application, such as a work order application 1121 to request a work order be provisioned via an application canvas 1120 of a cloud infrastructure environment 100.

In accordance with an embodiment, the work order 1121 can comprise, for example, both a work flow 1122 as well as indications of work order equipment instances 1123 that are to be utilized within the work order 1121. For example, a requested work order can comprise one or several work flows 1122 which can be utilized in connecting steps at and between various work order equipment instances 1123, each of which work order equipment instances are associated with equipment identifiers (e.g., equipment IDs), where each equipment ID is linked with, respectively, a distinct piece of equipment or asset 1110.

In accordance with an embodiment, the work flow 1122 and the work order equipment instances can be passed to a prediction engine (suggestion engine) 1130, which can request from the data associated with the work order equipment from monitoring sensors and data storage 1111 associated with the equipment/asset 1110.

In accordance with an embodiment, data associated with the work order equipment can comprise, for example, asset metadata, equipment usage metadata, meter readings of the asset, and data associated with instances of asset failure.

In accordance with an embodiment, the requested data associated with the work order equipment can comprise a request for data associated with the indicated work order equipment instances 1123 identified in the work order 1121 as well as data associated with similar or identical equipment instances. In this way, the data returned to the prediction/suggestion engine 1130 can comprise both data associated with the work order equipment instances identified in the work order 1121 as well as data associated with additional or other instances of similar or identical equipment that could potentially be substituted into the work order 1121.

For example, if the work order 1121 identified, by equipment ID, a first instance of a CNC machine to be utilized in the work order 1121, the request to equipment/asset 1110 by the prediction/suggestion engine can include a request for data associated with the identified first instance of the CNC machine (identified by its equipment ID), as well as data associated with additional/other CNC machines, each being identified by their own equipment ID, to which the user has access to.

In accordance with an embodiment, the data associated with the work order equipment, including data associated with the equipment identified in the work order as well as the additional/other equipment, can be transmitted to a machine learning/artificial intelligence component 1131, which can return a result comprising a prediction/suggestion associated with the work order equipment instance 1132. In certain embodiments, the machine learning/artificial intelligence component can utilize an algorithm, such as an XGBoost Algorithm.

In accordance with an embodiment, the prediction/suggestion engine 1130 can utilize certain data from the equipment/asset 1110 when generating the prediction/suggestion 1132. This data can include, for example, maintenance history associated with the work order equipment instances. Such maintenance history can comprise a record of all maintenance activities performed on each equipment instance, including preventive maintenance and repairs. The data can additionally comprise failure instances, which can comprise a log file of all equipment instance failures and the nature of these failures. The data can further comprise meter readings, which can comprise data from equipment sensors that track usage metrics such as operating hours, cycles completed, and load levels. The prediction/suggestion engine 1130 can utilize machine learning algorithms to determine whether a work order equipment instance chosen for a work order is a good choice.

In accordance with an embodiment, the prediction/suggestion associated with the work order equipment instance 1132 can comprise, for example, a prediction that, based upon the monitoring/sensor data retrieved from the equipment, that the selected work order equipment instance will not fail or otherwise go offline in association with running the work order 1121. A suggestion can also be provided in connection with such a prediction, where the suggestion comprises similar/other work order equipment instances that could be utilized within the workflow so as to maximize efficiency or lifespan of equipment/asset 1110.

In accordance with an embodiment, the prediction/suggestion associated with the work order equipment instance 1132 can comprise, for example, a prediction that, based upon the monitoring/sensor data retrieved from the equipment, that the selected work order equipment instance will fail or otherwise go offline in association with running the work order 1121. In such a case, a suggestion can also be provided in connection with such a prediction, where the suggestion comprises similar/other work order equipment instances that can be utilized within the workflow in place of the originally selected work order equipment instance 1121 so as to minimize the possibility of a failure in the work order 1121.

In accordance with an embodiment, while described in one embodiment above, it is generally understood that the described prediction/suggestion engine can be utilized at various points of time in a lifecycle of a work order.

For example, during work order creation, the prediction/suggestion engine can gather equipment data and calculate scores for all available instances. In such situations, the prediction suggestion engine can suggest top-scoring equipment instances for a resource operation. As well, the prediction/suggestion engine can be utilized during real-time validation. In such a case, the prediction/suggestion engine can check the latest meter readings in real time during performance of the work order. If a condition of the selected equipment instance has deteriorated, the prediction/suggestion engine can reevaluate and suggest an alternative. Such prediction/suggestion engine can also be utilized at execution time. In such a case, an operator/user uses the suggested equipment. The prediction/suggestion engine can continuously monitors meter readings and logs any anomalies or issues during operation.

FIG. 12 illustrates a system for use with a data analytics environment to determine probability of down failure or downtime in work orders, in accordance with an embodiment.

In accordance with an embodiment, a user can interact with a computer device 10 comprise computer hardware 11, which is running an application 14 that provides a user interface 12. The user can utilize an application, such as a work order application 1121 to request a work order be provisioned via an application canvas 1120 of a cloud infrastructure environment 100.

In accordance with an embodiment, the work order 1121 can comprise, for example, both a work flow 1122 as well as indications of work order equipment instances 1123 that are to be utilized within the work order 1121. For example, a requested work order can comprise one or several work flows 1122 which can be utilized in connecting steps at and between various work order equipment instances 1123, each of which work order equipment instances are associated with equipment identifiers (e.g., equipment IDs), where each equipment ID is linked with, respectively, a distinct piece of equipment/asset 1110.

In accordance with an embodiment, the work flow 1122 and the work order equipment instances can be passed to a prediction/suggestion engine 1130, which can request from the data associated with the work order equipment from monitoring sensors and data storage 1111 associated with the equipment/asset 1110.

In accordance with an embodiment, the requested data associated with the work order equipment can comprise a request for data associated with the indicated work order equipment instances 1123 identified in the work order 1121 as well as data associated with similar or identical equipment instances. In this way, the data returned to the prediction/suggestion engine 1130 can comprise both data associated with the work order equipment instances identified in the work order 1121 as well as data associated with additional or other instances of similar or identical equipment that could potentially be substituted into the work order 1121.

For example, if the work order 1121 identified, by equipment ID, a first instance of a CNC machine to be utilized in the work order 1121, the request to equipment/asset 1110 by the prediction/suggestion engine can include a request for data associated with the identified first instance of the CNC machine (identified by its equipment ID), as well as data associated with additional/other CNC machines, each being identified by their own equipment ID, to which the user has access to.

In accordance with an embodiment, the data associated with the work order equipment, including data associated with the equipment identified in the work order as well as the additional/other equipment, can be transmitted to a machine learning/artificial intelligence component 1131. The ML/AI component can work with a LLM 1210, which can either be hosted within the cloud infrastructure environment, or which can be communicatively coupled with the ML/AI component. The ML/AI component, in conjunction with the LLM 1210, can return a result comprising a prediction/suggestion associated with the work order equipment instance 1132.

In accordance with an embodiment, the prediction/suggestion engine 1130 can utilize certain data from the equipment/asset 1110 when generating the prediction/suggestion 1132. This data can include, for example, maintenance history associated with the work order equipment instances. Such maintenance history can comprise a record of all maintenance activities performed on each equipment instance, including preventive maintenance and repairs. The data can additionally comprise failure instances, which can comprise a log file of all equipment instance failures and the nature of these failures. The data can further comprise meter readings, which can comprise data from equipment sensors that track usage metrics such as operating hours, cycles completed, and load levels. The prediction/suggestion engine 1130 can utilize machine learning algorithms to determine whether a work order equipment instance chosen for a work order is a good choice.

In accordance with an embodiment, the prediction/suggestion associated with the work order equipment instance 1132 can comprise, for example, a prediction that, based upon the monitoring/sensor data retrieved from the equipment, that the selected work order equipment instance will not fail or otherwise go offline in association with running the work order 1121. A suggestion can also be provided in connection with such a prediction, where the suggestion comprises similar/other work order equipment instances that could be utilized within the workflow so as to maximize efficiency or lifespan of equipment/asset 1110.

In accordance with an embodiment, the prediction/suggestion associated with the work order equipment instance 1132 can comprise, for example, a prediction that, based upon the monitoring/sensor data retrieved from the equipment, that the selected work order equipment instance will fail or otherwise go offline in association with running the work order 1121. In such a case, a suggestion can also be provided in connection with such a prediction, where the suggestion comprises similar/other work order equipment instances that can be utilized within the workflow in place of the originally selected work order equipment instance 1121 so as to minimize the possibility of a failure in the work order 1121.

In accordance with an embodiment, while described in one embodiment above, it is generally understood that the described prediction/suggestion engine can be utilized at various points of time in a lifecycle of a work order.

For example, during work order creation, the prediction/suggestion engine can gather equipment data and calculate scores for all available instances. In such situations, the prediction suggestion engine can suggest top-scoring equipment instances for a resource operation. As well, the prediction/suggestion engine can be utilized during real-time validation. In such a case, the prediction/suggestion engine can check the latest meter readings in real time during performance of the work order. If a condition of the selected equipment instance has deteriorated, the prediction/suggestion engine can reevaluate and suggest an alternative. Such prediction/suggestion engine can also be utilized at execution time. In such a case, an operator/user uses the suggested equipment. The prediction/suggestion engine can continuously monitors meter readings and logs any anomalies or issues during operation.

FIG. 13 illustrates a system for use with a data analytics environment to determine probability of down failure or downtime in work orders, in accordance with an embodiment.

In accordance with an embodiment, a user can interact with a computer device 10 comprise computer hardware 13, which is running an application 14 that provides a user interface 12. The user can utilize an application, such as a work order application 1121 to request a work order be provisioned via an application canvas 1120 of a cloud infrastructure environment 100.

In accordance with an embodiment, the work order 1121 can comprise, for example, both a work flow 1122 as well as indications of work order equipment instances 1123 that are to be utilized within the work order 1121. For example, a requested work order can comprise one or several work flows 1122 which can be utilized in connecting steps at and between various work order equipment instances 1123, each of which work order equipment instances are associated with equipment identifiers (e.g., equipment IDs), where each equipment ID is linked with, respectively, a distinct piece of equipment/asset 1110.

In accordance with an embodiment, the work flow 1122 and the work order equipment instances can be passed to a prediction/suggestion engine 1130, which can request from the data associated with the work order equipment from a data warehouse instance 160, e.g., that is associated with the user. In certain embodiments, the data warehouse instance 160 can comprise an autonomous data warehouse instance.

In accordance with an embodiment, the data warehouse instance 160 can be provided with equipment/asset data 1300, e.g., periodically, continuously, or on a command to fetch data, or in any other similar manner.

In accordance with an embodiment, data associated with the work order equipment can comprise, for example, asset metadata, equipment usage metadata, meter readings of the asset, and data associated with instances of asset failure.

In accordance with an embodiment, the requested data associated with the work order equipment can comprise a request for data associated with the indicated work order equipment/asset instances 1123 identified in the work order 1121 as well as data associated with similar or identical equipment instances. In this way, the data returned to the prediction/suggestion engine 1130 can comprise both data associated with the work order equipment instances identified in the work order 1121 as well as data associated with additional or other instances of similar or identical equipment that could potentially be substituted into the work order 1121.

For example, if the work order 1121 identified, by equipment ID, a first instance of a CNC machine to be utilized in the work order 1121, the request to equipment/asset 1110 by the prediction/suggestion engine can include a request for data associated with the identified first instance of the CNC machine (identified by its equipment ID), as well as data associated with additional/other CNC machines, each being identified by their own equipment ID, to which the user has access to.

In accordance with an embodiment, the data associated with the work order equipment, including data associated with the equipment identified in the work order as well as the additional/other equipment, can be transmitted to a machine learning/artificial intelligence component 1131, which can return a result comprising a prediction/suggestion associated with the work order equipment instance 1132.

In accordance with an embodiment, the prediction/suggestion engine 1130 can utilize certain data from the equipment/asset 1110 when generating the prediction/suggestion 1132. This data can include, for example, maintenance history associated with the work order equipment instances. Such maintenance history can comprise a record of all maintenance activities performed on each equipment instance, including preventive maintenance and repairs. The data can additionally comprise failure instances, which can comprise a log file of all equipment instance failures and the nature of these failures. The data can further comprise meter readings, which can comprise data from equipment sensors that track usage metrics such as operating hours, cycles completed, and load levels. The prediction/suggestion engine 1130 can utilize machine learning algorithms to determine whether a work order equipment instance chosen for a work order is a good choice.

In accordance with an embodiment, the prediction/suggestion associated with the work order equipment instance 1132 can comprise, for example, a prediction that, based upon the monitoring/sensor data retrieved from the equipment, that the selected work order equipment instance will not fail or otherwise go offline in association with running the work order 1121. A suggestion can also be provided in connection with such a prediction, where the suggestion comprises similar/other work order equipment instances that could be utilized within the workflow so as to maximize efficiency or lifespan of equipment/asset 1110.

In accordance with an embodiment, the prediction/suggestion associated with the work order equipment instance 1132 can comprise, for example, a prediction that, based upon the monitoring/sensor data retrieved from the equipment, that the selected work order equipment instance will fail or otherwise go offline in association with running the work order 1121. In such a case, a suggestion can also be provided in connection with such a prediction, where the suggestion comprises similar/other work order equipment instances that can be utilized within the workflow in place of the originally selected work order equipment instance 1121 so as to minimize the possibility of a failure in the work order 1121.

In accordance with an embodiment, while described in one embodiment above, it is generally understood that the described prediction/suggestion engine can be utilized at various points of time in a lifecycle of a work order. For example, during work order creation, the prediction/suggestion engine can gather equipment data and calculate scores for all available instances. In such situations, the prediction suggestion engine can suggest top-scoring equipment instances for a resource operation. As well, the prediction/suggestion engine can be utilized during real-time validation. In such a case, the prediction/suggestion engine can check the latest meter readings in real time during performance of the work order. If a condition of the selected equipment instance has deteriorated, the prediction/suggestion engine can reevaluate and suggest an alternative. Such prediction/suggestion engine can also be utilized at execution time. In such a case, an operator/user uses the suggested equipment. The prediction/suggestion engine can continuously monitor meter readings and logs any anomalies or issues during operation.

FIG. 14 illustrates a system for use with a data analytics environment to determine probability of down failure or downtime in work orders, in accordance with an embodiment.

In accordance with an embodiment, a user can interact with a computer device 10 comprise computer hardware 11, which is running an application 14 that provides a user interface 12. The user can utilize an application, such as a work order application 1121 to request a work order be provisioned via an application canvas 1120 of a cloud infrastructure environment 100.

In accordance with an embodiment, the work order 1121 can comprise, for example, both a work flow 1122 as well as indications of work order equipment instances 1123 that are to be utilized within the work order 1121.

For example, a requested work order can comprise one or several work flows 1122 which can be utilized in connecting steps at and between various work order equipment instances 1123, each of which work order equipment instances are associated with equipment identifiers (e.g., equipment IDs), where each equipment ID is linked with, respectively, a distinct piece of equipment/asset 1110.

In accordance with an embodiment, the work flow 1122 and the work order equipment instances can be passed to a prediction/suggestion engine 1130, which can request from the data associated with the work order equipment from a data warehouse instance 160, e.g., that is associated with the user. In certain embodiments, the data warehouse instance 160 can comprise an autonomous data warehouse instance.

In accordance with an embodiment, the data warehouse instance 160 can be provided with equipment/asset data 1400, e.g., periodically, continuously, or on a command to fetch or pull data, or in any other similar manner.

In accordance with an embodiment, data associated with the work order equipment can comprise, for example, asset metadata, equipment usage metadata, meter readings of the asset, and data associated with instances of asset failure.

In accordance with an embodiment, the requested data associated with the work order equipment can comprise a request for data associated with the indicated work order equipment instances 1123 identified in the work order 1121 as well as data associated with similar or identical equipment instances. In this way, the data returned to the prediction/suggestion engine 1130 can comprise both data associated with the work order equipment instances identified in the work order 1121 as well as data associated with additional or other instances of similar or identical equipment that could potentially be substituted into the work order 1121.

For example, if the work order 1121 identified, by equipment ID, a first instance of a CNC machine to be utilized in the work order 1121, the request to equipment/asset 1110 by the prediction/suggestion engine can include a request for data associated with the identified first instance of the CNC machine (identified by its equipment ID), as well as data associated with additional/other CNC machines, each being identified by their own equipment ID, to which the user has access to.

In accordance with an embodiment, the data associated with the work order equipment, including data associated with the equipment identified in the work order as well as the additional/other equipment, can be transmitted to a machine learning/artificial intelligence component 1131. The ML/AI component can work with a LLM 1410, which can either be hosted within the cloud infrastructure environment, or which can be communicatively coupled with the ML/AI component. The ML/AI component, in conjunction with the LLM 1410, can return a result comprising a prediction/suggestion associated with the work order equipment instance 1132.

In accordance with an embodiment, the prediction/suggestion engine 1130 can utilize certain data from the equipment/asset 1110 when generating the prediction/suggestion 1132. This data can include, for example, maintenance history associated with the work order equipment instances. Such maintenance history can comprise a record of all maintenance activities performed on each equipment instance, including preventive maintenance and repairs. The data can additionally comprise failure instances, which can comprise a log file of all equipment instance failures and the nature of these failures. The data can further comprise meter readings, which can comprise data from equipment sensors that track usage metrics such as operating hours, cycles completed, and load levels. The prediction/suggestion engine 1130 can utilize machine learning algorithms to determine whether a work order equipment instance chosen for a work order is a good choice.

In accordance with an embodiment, the prediction/suggestion associated with the work order equipment instance 1132 can comprise, for example, a prediction that, based upon the monitoring/sensor data retrieved from the equipment, that the selected work order equipment instance will not fail or otherwise go offline in association with running the work order 1121. A suggestion can also be provided in connection with such a prediction, where the suggestion comprises similar/other work order equipment instances that could be utilized within the workflow so as to maximize efficiency or lifespan of equipment/asset 1110.

In accordance with an embodiment, the prediction/suggestion associated with the work order equipment instance 1132 can comprise, for example, a prediction that, based upon the monitoring/sensor data retrieved from the equipment, that the selected work order equipment instance will fail or otherwise go offline in association with running the work order 1121. In such a case, a suggestion can also be provided in connection with such a prediction, where the suggestion comprises similar/other work order equipment instances that can be utilized within the workflow in place of the originally selected work order equipment instance 1121 so as to minimize the possibility of a failure in the work order 1121.

In accordance with an embodiment, while described in one embodiment above, it is generally understood that the described prediction/suggestion engine can be utilized at various points of time in a lifecycle of a work order.

For example, during work order creation, the prediction/suggestion engine can gather equipment data and calculate scores for all available instances. In such situations, the prediction suggestion engine can suggest top-scoring equipment instances for a resource operation. As well, the prediction/suggestion engine can be utilized during real-time validation. In such a case, the prediction/suggestion engine can check the latest meter readings in real time during performance of the work order. If a condition of the selected equipment instance has deteriorated, the prediction/suggestion engine can reevaluate and suggest an alternative. Such prediction/suggestion engine can also be utilized at execution time. In such a case, an operator/user uses the suggested equipment. The prediction/suggestion engine can continuously monitor meter readings and logs any anomalies or issues during operation.

FIG. 15 illustrates a screenshot produced by a system for use with a data analytics environment to determine a probability of failure or downtime in work orders, in accordance with an embodiment.

In accordance with an embodiment, a cloud infrastructure environment 100 can produce, for display 1500, a display comprising a first portion comprising an indication of work orders and assets related thereto 1505. The display can further comprise a second portion comprising an indication of a result of an asset check 1510.

In accordance with an embodiment, the systems and methods can receive an indication of one or more work orders on which to perform a check to determine a probability of failure or down time based upon an asset, or assets, to be utilized by the selected work order(s).

In accordance with an embodiment, as shown in FIG. 15, the check performed on Work Order RT-A-Job1 resulted in a prediction that the asset, namely Test_Asset_IoT_1 is expected to operate successfully during the running of Work Order RT-A-Job1.

FIG. 16 illustrates a screenshot produced by a system for use with a data analytics environment to determine a probability of failure or downtime in work orders, in accordance with an embodiment.

In accordance with an embodiment, a cloud infrastructure environment 100 can produce, for display 1600, a display comprising a first portion comprising an indication of work orders and assets related thereto 1605. The display can further comprise a second portion comprising an indication of a result of an asset check 1610.

In accordance with an embodiment, the systems and methods can receive an indication of one or more work orders on which to perform a check to determine a probability of failure or down time based upon an asset, or assets, to be utilized by the selected work order(s).

In accordance with an embodiment, as shown in FIG. 16, the check performed on Work Order RT-A-Job1 resulted in a prediction that the asset, namely Test_Asset_IoT_1 is expected to fail during the running of Work Order RT-A-Job1. As such, check 1410 provides a prompt and a link for a user to go the application, such as a Fusion Application, to reconfigure the work order to select an alternative instance of a similar asset as Test_Asset_IoT_1 to be utilized in the work order.

FIG. 17 is a flowchart of a method for use with a data analytics environment to determine a probability of failure or downtime in work orders, in accordance with an embodiment.

In accordance with an embodiment, at step 1710, the method can provide, by a computer including one or more processors, access to a data analytics environment.

In accordance with an embodiment, at step 1720, the method can provide a work order application at the data analytics environment, the work order application providing a work order canvas at which a work order comprising an instance of a work order asset is identified.

In accordance with an embodiment, at step 1730, the method can generate, by a prediction engine of the data analytics environment, an indication of a likelihood of success of the work order, wherein the prediction engine utilizes data associated with the instance of the work order asset to provide the indication of the likelihood of success.

In accordance with an embodiment, at step 1740, the method can provide the indication of the likelihood of success of the work order via an interface.

In accordance with various embodiments, the systems and methods described herein can be implemented using one or more computer, computing device, machine, or microprocessor, including one or more processors, memory and/or computer readable storage media programmed according to the teachings of the present disclosure. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those skilled in the software art.

In some embodiments, the teachings herein can include a computer program product which is a non-transitory computer readable storage medium (media) having instructions stored thereon/in which can be used to program a computer to perform any of the processes of the present teachings. Examples of such storage mediums can include, but are not limited to, hard disk drives, hard disks, hard drives, fixed disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, or other types of storage media or devices suitable for non-transitory storage of instructions and/or data.

The foregoing description has been provided for the purposes of illustration and description. It is not intended to be exhaustive or to limit the scope of protection to the precise forms disclosed. Many modifications and variations will be apparent to the practitioner skilled in the art. For example, although several of the examples provided herein illustrate use with cloud environments such as Oracle Analytics Cloud; in accordance with various embodiments, the systems and methods described herein can be used with other types of enterprise software applications, cloud environments, cloud services, cloud computing, or other computing environments.

The embodiments were chosen and described in order to best explain the principles of the present teachings and their practical application, thereby enabling others skilled in the art to understand the various embodiments and with various modifications that are suited to the particular use contemplated. It is intended that the scope be defined by the following claims and their equivalents.